1. Latent Friend Mining from Blog Data (ICDM’06)

2. Outline About the Blog Problem statement Three kinds of Approaches Experiment Conclusion

3. About the Blog Our goal in this research is to develop methods for mining the potential relationship among people, and we call this problem “latent friend detection”.

4. Problem statement Our goal in this paper is to find latent friends from the blog data. The entries, bloggers give their basic information as well as much interesting information on the blogs. Take MSN spaces as an example, the bloggers may put their favorite songs, sports, pictures on the blogs.

5. Three kinds of Approaches (Method 1) Cosine Similarity-based Method straightforward solution for the latent friend detection problem is to calculate the similarity between the contents of the entries from two bloggers.

6. Three kinds of Approaches (Method 1 cont.) Cosine Similarity-based Method where is the term frequency of term k in blogger i’s blog. Given a blogger i, after calculating the similarity between him/her and all other bloggers, we can sort the blog-gers according to the similarity. Then the top bloggers in the list can be recommended as blogger i’s latent friends.

7. Three kinds of Approaches (Method 1 cont.)

8. Three kinds of Approaches (Method 2) Topic Model based Method We define the distance between blogger i and blogger j as the topic distributions conditioned on each blogger: where T is the number of topics; θit is the probability of opic t conditioned on blogger i.

9. Three kinds of Approaches (Method 3) Two-Level Similarity-based Method Coarse Similarity Calculation Lifestyle Food :Chinese food, Western food

10. Three kinds of Approaches (Method 3 cont.) Two-Level Similarity-based Method Finer Similarity at Literal Level

11. Experiment

12. Conclusion (1) The paper put forward a new research problem of finding latent friends from blog data (2) A novel two-level similarity-based approach is proposed to solve the problem effectively and efficiently, which takes into account the time related content information as well as the topic-distribution information.